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<title>fMRI Sandbox Help</title>
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<h1>fMRI Sandbox</h1>


<h2>General idea:</h2>
The software that is commonly used for fMRI data analysis is often not very interactive or intuitively useable. Usually, it is necessary to already know the parameters for various image processing steps in advance and entering them either manually or predefining them in a script in order to run them automatically. This requires quite a lot of knowledge of the principles of fMRI data processing and the properties of the data analyzed.
In some instances, it is necessary to analyze data whose quality is not known in advance, and to do that much faster than most standard imaging packages will allow, e.g. in cases where scan parameters have to be changed in between scans, depending on the quality of data generated.
In those cases, a quick and dirty way of exploring the generated data might be desirable.
It is for those cases that this program suite was written.
It allows the user to quickly read MRI data into a graphical user interface (GUI), and display and manipulate such data. When simultaneously loading at least one hrf predictor, it is possible to see with little delay the effects that modifications to the data processing steps have on the predicted results with a standardized analysis software.
fMRI Sandbox is by no means intended to replace tried and tested software such as SPM, FSL or BrainVoyager that is currently used for fMRI analyses.

<h2>Installation:</h2>
fMRI Sandbox is a Matlab GUI. You need to make it part of your Matlab path, and it will run. Just put the sandbox directory somewhere on your computer and set the Matlab path so that Matlab can access sandbox and its subdirectories. Sandbox currently runs on Matlab 2007b to 2010. In order to use some of the SPM functionality, you;
Please be aware that Sandbox handles everything in main memory, so a 64 bit OS is recommended. Using a 32bit-OS is also possible, but may result in out of memory errors.

<h2>Starting it up:</h2>
There are basically two ways to start up Sandbox:
<ol>
<li>Simply type FSB as a command in Matlab.
A window will pop up whose contents are explained in the following paragraphs</li>
<li>If you happen to work at the Department for Biological Cybernetics in Tuebingen, you can also type FSB followed by the scan name and number to load files directly from the Bruker scanner in our lab,
e.g. FSB('E06.P31',10). You may need to add your path in the set_stdpath.m to directly access the scanner directory.</li>
</ol>

<h2>The file format:</h2>
<p>Here you find more information on: <a href="file_format.html">The file formats</a> that fMRI Sandbox uses</p>

<h2>A short how-to:</h2>
<p>Here you find a short howto: <a href="how_to_proceed.html">How to proceed</a></p>

<h2>The buttons explained:</h2>
<p>
For detailed information on each of the many buttons in fMRI Sandbox, see the <a href="buttons_explained.html">Buttons explained</a>
</p>

<h2> Outline:</h2>

<img src="sandbox_image.jpg" width="666" height="500" alt="">

The GUI is divided into 3 sections, the upper left being the part where
buttons and sliders are positioned that allow you to perform operations
on the image(s) displayed in the figures below that output statistical
values on the upper right side.
The figures display three sections of your MR volume(s) in the upper and
left figure. At the bottom right, the timecourse of your volumes is
displayed, if you are working with a 4-D dataset, otherwise the space is
empty.

<h2>The figures section:</h2>

Directly below all the buttons, you can see three figure windows when the GUI is launched.
Once you <a href="#load">load</a> a scan, this is displayed in these windows in the axial,
sagittal and coronal plane. Use the sliders or simply click into the figures to see different slices
of the brain. Use the <a href="#col">color menu</a> to select a color scheme that suits your needs best.
Should you load a functional scan either as a 4D-volume or as a <a href="#multload">sequence of 3D-volumes</a>,
a fourth figure comes up, in which the temporal dimension of the data is displayed. Use the slider or
click into that figure in order to jump around in scans acquired over time.
The thin magenta line indicates the signal in the voxel you have selected by clicking into any of the figures as a function of time.
The green line equates the mean signal intensity in the green ROI as a function of time.
Should you have produced something like an intrial vector which indicates which volumes are part of trials,
a third window indicating the percent signal change in your ROI over the respective trial time points will come up.

<h2>The statistics section</h2>

If you have loaded a <a href="#pred">predictor</a> in the form of an assumed hemodynamic response
to the experiment you are carrying out, you will see a fifth figure on the right of the button area.
In this figure, results of statistical calculations are displayed. In your timeline window, you will
additionally see another thin red line representing one of your predictors. The statistics is
calculated for the relationship between your predicted hemodynamic response (the lower red line)
and the actually observed hemodynamic response (the upper magenta line), and is valid only
for the single point that you have chosen with a mouse click or slider movement.
Once you click on the <a href="#dispmap">"Display map"</a> button, you will see a statistical map
overlaid on the three section images. Choose with the <a href="#model">"Model" </a> dropdown menu
which kind of map you want overlaid on the brain images. The fastest map is a pure correlation map.
Determine the threshold of the map with the <a href="#corrslid">Threshold slider</a>, and the
minimum number of contiguous voxels in a voxel cluster with the <a href="#clusslid">cluster slider</a>.
Correlation matrix has the additional advantage of showing negative (blue) and positive correlations (red)
between predictors and variables.
If you need more detailed information, you can also select different models with the <a href="#model">"Model" </a>dropdown menu. <br>
These other models are considerably slower as they are computationally more intensive.
GLM is usually slowest, and not necessarily most accurate.
A viable alternative is Robust Regression, which is not as slow but possibly more accurate
because it uses an iterative regression algorithm to excludes outliers from its calculations.




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